DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 14, 2026 has been entered.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-13 are to a machine and claims 14-20 are to a process.
Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations that fall into the grouping of mental processes and/or mathematical concepts:
1. A system, comprising:
a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer;
a cloud server including a server processor which:
receives the spectrometer data generated by the spectrometer from the processor,
analyzes the spectrometer data,
identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample, compares the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model; determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model; and
updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data; and identifies the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model.
14. (Currently Amended) A method, comprising:
receiving, by a processor, spectrometer data representative of a scanned sample and generated by a spectrometer;
analyzing, by the processor, the spectrometer data;
identifying, by the processor and based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample,
comparing, by the processor, the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model;
determining, by the processor, that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model;
updating, by the processor, the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data; and
identifying, by the processor, the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model.
The above bolded limitations recite an abstract idea of a mental process, as a person can mentally perform analysis and evaluate received spectrometer data. A person can mentally form a judgement to recognize and identify one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample. The comparison of the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model, at a basic level, amounts to a comparison between two sets of data, which can be performed mentally by a person or by pen and paper to determine whether a match occurs. Depending on the complexity of the machine learning model, the comparing of the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model could alternatively amount to a mathematical concept to carry out the numerical calculations to calculate a comparison result between two sets of data. The limitation of "determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model," amounts to mental process to form an informational-based judgement based on an evaluation between the data characteristics of the scanned sample and the known sample, or can amount to mathematically-based difference comparisons. The limitation of "updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data" amount to a mathematical concept to numerically update the values of a machine learning model. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.) Thus, the updating of a model amounts to a mathematical concept to update the variables and values of the model. The limitation of "identifies the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model," amounts to a a mental process to form a judgement or classification of the scanned sample to identify it as counterfeit, not counterfeit, or changed.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the receiving of spectrometer data representative of a scanned sample and generated by a spectrometer amounts to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); and (2) the system, processor, cloud server including a server processor, and machine learning application amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Taken as a whole, the independent claims do not provide a further practical application beyond the obtaining of an updated “data”-based machine learning model, as such an abstract data-based model is not further used to carry out any physical application to update, correct, or transform a technology or technical process for an improvement to the technology or technical field.
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The receiving of spectrometer data generated by the spectrometer does not add something significantly more because similar to above, such limitations amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and do not describe any gathering of data using an unconventional measurement arrangement. The elements of a processor, cloud server including a server processor, and machine learning application do not add something significantly more because they amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Dependent claims 2-8, 13, 15-16 contain additional limitations that amount to merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), dependent claims 9-11 and 17-20 describe further mental process data analysis steps that can equivalently be performed by a person that are part of the judicial exception itself, and dependent claim 12 describes further post-solution display activity that is not indicative of integration into a practical application nor something significantly more than the recited judicial exception (see MPEP 2016.05(g)).
4. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the updated machine learning model or counterfeit status determination to drive a physical update, physical transformation/change, or physical repair/maintenance of a technology or technical process, could provide integration into a practical application to demonstrate an improvement to the technology or technical field. For example, practical applications (beyond further based data-determinations) that amount to an integration into a practical application include using the updated machine learning model carry out physical seizing of determined counterfeit products from entry (see published specification paragraph [0028]) or physical preventing of the sale of counterfeited items (see published specification paragraph [0029]).
Allowable Subject Matter
5. Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: Claim 1 contains allowable subject matter because the closest prior art, Guzman Cardozo (US Pat. Pub. 2019/0310207) fails to anticipate or render obvious a system, comprising: a cloud server including a server processor which: determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model; updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data, in combination with the rest of the claim limitations as claimed and defined by the Applicant. Claim 14 contains allowable subject matter because the closest prior art, Guzman Cardozo (US Pat. Pub. 2019/0310207) fails to anticipate or render obvious a method, comprising: determining, by the processor, that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model; updating, by the processor, the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data, in combination with the rest of the claim limitations as claimed and defined by the Applicant.6. Dependent claims 2-13 depend from claim 1 and contain allowable subject matter for at least the same reasons as given for claim 1. Dependent claims 15-20 depend from claim 14 and contain allowable subject matter for at least the same reasons as given for claim 14.
Response to Arguments
7. Applicant’s arguments, see Applicant’s Arguments/Remarks, filed January 14, 2026, with respect to the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive.
8. Applicant argues that at least claim 1 is drawn to eligible and allowable subject matter at least based on reciting a practical application of determining whether or not a "scanned sample is or is not counterfeit relative to the sample known to the machine learning model." Further, Applicant respectfully submits that claim 1 further recites a "cloud server including a processor which…identifies the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model." Applicant respectfully submits that determining that a "scanned sample" is or is not "counterfeit," and identifying the "scanned sample" as being counterfeit, not counterfeit, or changed" as recited in claim 1 is not merely an abstract idea. Applicant argues that rather, claim 1 recites a practical application of a machine learning model to determine and identify potentially counterfeit samples of real-world products as discussed in paragraph [0025] of Applicant's Specification. Applicant makes similar arguments for claim 14 and the remaining dependent claims as made for claim 1 (see Applicant's Arguments/Remarks 1/14/2026, pgs. 8-9).
9. In response, the Examiner respectfully disagrees and points out that the scanned sample being "counterfeit," "not counterfeit," or "changed," are all informational-based classifications of the scanned sample. In other words, determining whether a scanned sample is or is not counterfeit, or identifying the scanned sample as being counterfeit, not counterfeit, or changed, are all limitations that amount to abstract idea mental processes or mathematical calculations to solve for an informational-based condition or attribute of the scanned sample. The analysis of the EPG Court is particularly applicable to the claims in the present case: "Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category," (see Electronic Power Group, LLC v. Alstom, 830 F. 3d 1350, 119 U.S.P.Q. 2d 1739 (Fed. Cir. 2016) at pg. 7). The present claims stop at an information-based result of whether the scanned sample is counterfeit, not counterfeit, or changed. This is an informational-based property of the scanned sample that may be useful to "know," but no further "integration into a practical application" has been recited in the claims. In Diamond v. Diehr, the claims did not merely stop at obtaining the informational-based result of a rubber curing time, but tied the claims into a practical application of physically operating a rubber molding press. Similarly, the Examiner respectfully suggests adding claim limitations in the present application that would provide an integration into a practical application, such as limitations describing further physical applications using the counterfeit status determination to drive a physical update, physical transformation/change, or physical repair/maintenance of a technology or technical process. For example, practical applications (beyond further based data-determinations) that amount to an integration into a practical application include using the updated machine learning model carry out physical seizing of determined counterfeit products from entry (see published specification paragraph [0028]) or physical preventing of the sale of counterfeited items (see published specification paragraph [0029]).
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 2/2/2026